Towards Climate Awareness in NLP Research

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Towards Climate Awareness in NLP Research. / Hershcovich, Daniel; Webersinke, Nicolas; Kraus, Mathias; Bingler, Julia Anna; Leippold, Markus.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2022. p. 2480-2494.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Hershcovich, D, Webersinke, N, Kraus, M, Bingler, JA & Leippold, M 2022, Towards Climate Awareness in NLP Research. in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 2480-2494, 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, 07/12/2022. <https://aclanthology.org/2022.emnlp-main.159>

APA

Hershcovich, D., Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2022). Towards Climate Awareness in NLP Research. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 2480-2494). Association for Computational Linguistics. https://aclanthology.org/2022.emnlp-main.159

Vancouver

Hershcovich D, Webersinke N, Kraus M, Bingler JA, Leippold M. Towards Climate Awareness in NLP Research. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2022. p. 2480-2494

Author

Hershcovich, Daniel ; Webersinke, Nicolas ; Kraus, Mathias ; Bingler, Julia Anna ; Leippold, Markus. / Towards Climate Awareness in NLP Research. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2022. pp. 2480-2494

Bibtex

@inproceedings{8a762fa37abf47ff816c729ab76c8632,
title = "Towards Climate Awareness in NLP Research",
abstract = "The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact, and present a quantitative survey to demonstrate this. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.",
author = "Daniel Hershcovich and Nicolas Webersinke and Mathias Kraus and Bingler, {Julia Anna} and Markus Leippold",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",
year = "2022",
language = "English",
pages = "2480--2494",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
publisher = "Association for Computational Linguistics",

}

RIS

TY - GEN

T1 - Towards Climate Awareness in NLP Research

AU - Hershcovich, Daniel

AU - Webersinke, Nicolas

AU - Kraus, Mathias

AU - Bingler, Julia Anna

AU - Leippold, Markus

N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.

PY - 2022

Y1 - 2022

N2 - The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact, and present a quantitative survey to demonstrate this. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.

AB - The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact, and present a quantitative survey to demonstrate this. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.

UR - http://www.scopus.com/inward/record.url?scp=85149436101&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:85149436101

SP - 2480

EP - 2494

BT - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

Y2 - 7 December 2022 through 11 December 2022

ER -

ID: 339849096